# A Physics-Inspired Deep Learning Framework with Polar Coordinate Attention for Ptychographic Imaging

arXiv: https://arxiv.org/abs/2412.06806 · HTML: https://mlsanity.com/paper/2412.06806/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 17 of 17)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [A Survey on Visual Transformer](https://mlsanity.com/paper/2012.12556/) (2020)
- [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](https://mlsanity.com/paper/2112.01526/) (2021)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer](https://mlsanity.com/paper/2301.11798/) (2023)
- [BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once](https://mlsanity.com/paper/2405.12971/) (2024)
- [ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling](https://mlsanity.com/paper/2307.01909/) (2023)
- [Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation Model](https://mlsanity.com/paper/2211.10590/) (2022)
- [Deep learning at the edge enables real-time streaming ptychographic imaging](https://mlsanity.com/paper/2209.09408/) (2022)
- [Learning to Synthesize: Robust Phase Retrieval at Low Photon counts](https://mlsanity.com/paper/1907.11713/) (2019)
- [PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction](https://mlsanity.com/paper/2310.07504/) (2023)
- [PtychoFormer: A Transformer-based Model for Ptychographic Phase Retrieval](https://mlsanity.com/paper/2410.17377/) (2024)
- [Noise-robust latent vector reconstruction in ptychography using deep generative models](https://mlsanity.com/paper/2311.07580/) (2023)
- [Using a modified double deep image prior for crosstalk mitigation in multislice ptychography](https://mlsanity.com/paper/2102.00869/) (2021)

## Built on by (most cited first, 4 of 4)

- [Towards generalizable deep ptychography neural networks](https://mlsanity.com/paper/2509.25104/) (2025)
- [Circular Phase Representation and Geometry-Aware Optimization for Ptychographic Image Reconstruction](https://mlsanity.com/paper/2604.26664/) (2026)
- [Zero-Shot Cross-Material Ptychographic Phase Reconstruction Using Deep Learning](https://mlsanity.com/paper/2609.13969/) (2026)
- [Real-Time Atomic-Resolution Electron Phase Imaging without Probe Calibration via Ptychography-Supervised Learning](https://mlsanity.com/paper/2609.25684/) (2026)

## Abstract (arXiv)

Ptychographic imaging confronts inherent challenges in applying deep learning for phase retrieval from diffraction patterns. Conventional neural architectures, both convolutional neural networks and Transformer-based methods, are optimized for natural images with Euclidean spatial neighborhood-based inductive biases that exhibit geometric mismatch with the concentric coherent patterns characteristic of diffraction data in reciprocal space. In this paper, we present PPN, a physics-inspired deep learning network with Polar Coordinate Attention (PoCA) for ptychographic imaging, that aligns neural inductive biases with diffraction physics through a dual-branch architecture separating local feature extraction from non-local coherence modeling. It consists of a PoCA mechanism that replaces Euclidean spatial priors with physically consistent radial-angular correlations. PPN outperforms existing end-to-end models, with spectral and spatial analysis confirming its greater preservation of high-frequency details. Notably, PPN maintains robust performance compared to iterative methods even at low overlap ratios, making it well suited for high-throughput imaging in real-world acquisition scenarios for samples with consistent structural characteristics.